Nature Neuroscience

Challenges in verbal communication are a prominent feature of autism. However, gene regulatory programs in speech-related cortical regions remain poorly characterized. In parallel, it remains unclear whether the heterogeneous genetic factors underlying autism converge on shared neurobiological mechanisms. To address these gaps, we generated paired transcriptomic and epigenomic data from post-mortem human brain tissue across 100 donors. Here, we show that transcriptional differences in the speech-related Brodmann Area 22 in individuals with neurodevelopmental conditions, including autism, are strongest among those with a known genetic diagnosis. A similar but attenuated signature is observed in those without a genetic diagnosis. These transcriptional differences are most pronounced in neurons, with glutamatergic L4/5 intratelencephalic neurons affected across multiple modalities. Finally, multimodal analysis implicates altered RFX3-dependent networks as a central hub in autism, particularly among L4/5 intratelencephalic neurons in non-verbal individuals. Together, our study identifies regulatory architecture linking chromatin state, transcriptional output, and variation in verbal ability in autism.

Neural synchrony, or correlated neural activity across interacting individuals, scales with relationship quality in humans, yet how it evolves during social bond formation remains unknown. Using fiber photometry in monogamous prairie voles, we track prefrontal cortex synchrony across pair bond formation. Bonded voles show stronger synchrony with partners than strangers, mirroring human findings. A linear mixed model reveals that synchrony is jointly shaped by bond strength, inter-animal distance, and time since interaction onset, with relationship type modulating how each factor contributes. Using a machine-learning behavioral classification pipeline we developed for freely interacting voles, we demonstrate that the coupling between specific behaviors and synchrony depends on the nature of the dyadic relationship. These findings establish that neural synchrony is not a simple function of proximity or interaction time but is fundamentally shaped by relationship history--a conclusion with direct implications for understanding the synchrony in human social attachment.

Sporadic amyotrophic lateral sclerosis (sALS) lacks longitudinal molecular measurements, making it difficult to distinguish early disease-organising changes from downstream consequences. We present a training-free framework that extracts directional structure from static single-nucleus RNA-seq by applying discrete Hodge decomposition to gene co-expression dynamics across pseudotime-ordered donor states. The framework separates irreversible co-expression cascades from circular feedback structure and regresses out the component explained by the healthy co-expression network, allowing disease-specific organisation to be examined in isolation. Perturbation benchmarks show that experimentally imposed sources are recoverable from control-normalised off-diagonal covariance-response fields, whereas marginal variance and diagonal covariance controls do not recover the source. Applied to sALS primary motor cortex (24 donors, 10 cell types), the framework identifies oligodendrocytes as the most structurally upstream cell type and upper-motor-neuron-containing layers as the most structurally downstream (Oligo cell-type{varphi} = 0.900, with glial cell types preserving the healthy co-expression network topology, whereas neuronal cell types show collapse-dominant deformation). Cytoplasmic translation is the only pathway with reproducible cross-cell-type upstream enrichment. At the gene level, the ribosome-associated quality-control factor NEMF -- which appends C-terminal alanine-threonine tags ("CATylation") to nascent chains on stalled ribosomes -- shows disease-specific loss of co-expression coherence in seven of ten cell types despite essentially unchanged mRNA expression; the disease signal is decoupling from collision-response partners (GCN2, PKR), not expression-level change. Cross-cohort validation across three BA4 motor cortex cohorts (including two external cohorts; total N=107) reproduced the oligodendrocyte-upstream / upper-motor-neuron-downstream structural architecture (Oligo-preserved / ET-sink) in all three cohorts, with NEMF co-expression coherence loss replicated in two of three cohorts. These data support a brain-side, circuit-distal structural model in which oligodendrocyte-lineage stress occupies an upstream-like preserved compartment, while upper-motor-neuron-containing excitatory populations form a downstream sink. The pattern is consistent with -- but does not directly establish -- a cascade architecture in which oligodendrocyte stress structurally precedes motor neuron TDP-43 pathology, and would produce a clinical phenotype resembling dying-back (the conventional view of ALS, in which motor neuron pathology appears to begin at distal axons and spread retrogradely toward the cell body) yet originating centrally and glially. NEMF/CATylation network disruption is identified as a candidate intermediate structural node bridging oligodendrocyte stress and motor neuron TDP-43 pathology.

Adaptive learning requires distinguishing environmental volatility from observation stochasticity, two sources of uncertainty that demand opposite adjustments to the learning rate but inflate experienced variance similarly. Disentangling them is computationally difficult with no tractable closed-form solution. Particle-filter methods are the natural tool for this kind of joint inference, but their stochastic likelihoods and non-differentiable objectives force derivative-free fitting protocols and discourage the individual-difference analyses central to cognitive modeling, where small effect sizes leave little room for additional estimator noise. We introduce the Categorical Bayes Filter (CBF), a deterministic alternative that preserves the conditional structure of recent particle-filter accounts but replaces the stochastic outer layer with a categorical distribution on a quantile grid parameterized through differentiable Beta quantile functions. The procedure performs evidence maximization with an exact, deterministic marginal likelihood that is fully differentiable in the grid parameters. In a volatility-stochasticity task with N = 643 participants, fitted CBF dispersion parameters reveal a cross-over phenotyping pattern between volatility-blind and stochasticity-blind subjects that is not recoverable from particle-filter parameters fit to the same data under a state-of-the-art protocol. The deterministic structure also yields a trial-by-trial ambiguity signal that predicts response times not used in fitting. More broadly, the approach opens individual-level analyses in cognitive modeling and computational psychiatry that stochastic methods have effectively foreclosed.

Neurons interact at synapses, but they also communicate through physical contact and proximity, including diffusion, glia-mediated interactions, and ephaptic coupling. Standard connectomes map synapses, but cannot capture the full set of cell-cell contacts that can support these pathways. Here we extract contactomes from two large mouse visual cortex volumes at nanoscale resolution and quantify every cell-cell contact, the shared surface area of each contact, and the relationship between contact and synaptic connectivity. We find that contactomes are 5 - 10x denser than synaptic graphs, revealing that neurons physically contact a much larger set of potential neighbors than they synaptically connect to. We further find that most nearby potential neighbors are already in physical contact, indicating that local structural change would add few new candidate synaptic partners. Finally, we find that astrocytes form a single large syncytium-like network that spans the tissue and directly contacts nearly all neurons, and that glial processes lie within a micron or two of almost every synapse, indicating that synapses reside within a pervasive glia-shaped microenvironment. Together, these results show that physical contact forms a distinct layer of brain architecture that extends far beyond the synaptic connectome.

Axons are the brains wiring, organized into bundles that connect nearby and distant regions. Axon caliber determines signal conduction velocity and varies both within and across bundles, reflecting the brains diverse functional demands. Much of what we know about this organization derives from 2D histology, assuming cylindrical axons whose calibers are described by their radius. Yet, recent 3D histology reveals that the radius varies along an individual axon--with implications for both characterizing axon caliber and potentially conduction velocity predictions. We show in 450,000 3D rat axon reconstructions that--despite this individual variation--axon bundles possess stable radius distributions at the ensemble level, which 2D cross-sections faithfully represent. This representativeness extends to conduction velocity predictions, as along-axon variation has only modest impact. In particular, large axons exhibit especially stable conduction, emphasizing their key role in time-critical signaling. With 2D sampling validated, we leverage 46 million human corpus callosum axons from 2D histology to determine sample size requirements across neuroscience applications. Our findings reinforce decades of 2D histology-based research on axon organization and its functional implications, while guiding future study design.

Cortical networks compute with remarkably sparse spiking activity, yet the circuit mechanisms that organize these few spikes into flexible, condition-dependent temporal codes remain poorly understood. Here we combine analyses of large-scale mouse recordings with a data-driven cortical microcircuit model (CMM) of mouse V1. In recordings, V1 activity exhibits condition-dependent spike-order sequences: peak-latency order varies with task outcome, current image identity, and preceding image identity, while remaining stable under split-half and single-trial analyses. After task optimization by backpropagation through time, the CMM reproduces this sequence-level signature and sparse activity more closely than the matched randomly connected RSNN and rate-RNN controls tested here. Ablations indicate that neuronal heterogeneity and distance-dependent local connectivity each reduce rigid sequential activity, with their combination giving the closest match to measured cortical signatures. Low-dimensional trajectory visualizations and model-silencing experiments further identify high-mutual-information early neurons whose removal perturbs task trajectories and decisions. Together, these results identify a biologically grounded computational principle: neuronal diversity and local connectivity help sparse recurrent networks avoid rigid temporal pipelines and support flexible, condition-dependent spike-order computation, providing candidate design principles for SNNs that exploit flexible temporal codes.

A defining feature of substance use disorder is that repeated drug use does not always lead to addiction, motivating the search for biomarkers of vulnerability1. Reduced striatal dopamine D2/3 receptor availability is a robust PET correlate of problematic stimulant use2-5, but the signal may reflect high endogenous dopamine level, and it conflates presynaptic D2 autoreceptors on dopamine axons with postsynaptic D2/3 heteroreceptors on striatal projection neurons. We dissociated these contributions using cell type-specific Drd2 haploinsufficiency in dopamine neurons (autoD2KD), D2-expressing medium spiny neurons (MSN-D2KD), or both. Autoreceptor haploinsufficiency (autoD2KD) weakened presynaptic control of dopamine release, enhanced phasic gain, and prolonged cocaine-evoked dopamine elevations. This was accompanied by a hyper-exploratory trait and altered cocaine adaptation. Specifically, autoD2KD mice showed greater cocaine-seeking behavior, despite intact responses to sucrose reward and punishment. Although all genotypes showed graded reductions in striatal D2/3 binding, D1-like compensations diverged, resulting in different D1:D2/3 ratio in the striatum. The clinical implication is that striatal D1 density and D1:D2/3 balance may emerge as critical biomarkers for distinguishing cell-type-specific D2 reductions relevant to addiction vulnerability.

Brain - computer interfaces (BCIs) require rapid and accurate decoding of neural activity, yet conventional computing architectures face growing latency as neural recording scales. We demonstrate a quantum computing - enabled neural decoding approach using a physical 1000-qubit coherent photonic Ising machine, in which inference is performed through hardware energy relaxation rather than numerical computation. By mapping sparse neural spike patterns onto Ising Hamiltonians, our hardware-native Quantum Semi-Restricted Boltzmann Machine achieves up to 96.2% accuracy across public in vivo datasets spanning multiple species and modalities. We report hardware-verified median latencies of 0.075 ms-- a tenfold speedup over GPUs--with complexity-invariant scaling. These results establish quantum computing as a viable pathway toward ultra-low-latency neural decoding for future BCI systems.

Face-to-face communication relies on the seamless integration of visual and acoustic cues, yet the spatiotemporal principles governing how the human brain dynamically represents and combines these multisensory streams remain largely unresolved. To address this, we recorded high-density electrocorticography (ECoG) from eight participants perceiving matched audiovisual, audio-only, and video-only continuous natural Mandarin speech. Using time-frequency-resolved encoding models, we reveal complementary, frequency-dependent integration regimes across the temporal lobe. We show that the superior temporal gyrus (STG) implements a feature-selective, auditory-dominant strategy, utilizing visual input to selectively strengthen low-frequency representations of lip-reading kinematics. Conversely, the middle temporal gyrus (MTG) acts as a higher-order multisensory hub, employing a frequency-selective strategy to broadly integrate diverse facial and articulatory features. Crucially, we demonstrate that access to visual information during perception significantly improves the acoustic and lexical accuracy of neural speech decoding and re-synthesis, with the MTG driving the largest gains in linguistic intelligibility. These findings uncover the dissociable neural architectures supporting robust multisensory perception, providing critical mechanistic insights for the development of next-generation, multimodal brain-computer interfaces.

Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking withdrawal to increased drug-taking remain poorly understood. Here, we use multisite local field potential recordings and interpretable machine learning to identify large-scale brain networks engaged by repeated opioid exposure and withdrawal. After discovering that repeated fentanyl exposure induces a progressively ramping network of widespread high beta and low gamma oscillations, we then identified a distinct brain network that selectively encodes the emergence and severity of opioid withdrawal. This network, termed EN-Withdrawal, is characterized by regional gamma oscillations and widely synchronized delta/theta oscillations. Its activity patterns predict the emergence of spontaneous and naloxone-precipitated withdrawal across multiple independent cohorts, generalizing across mice, sex, opioids, and dosing regimens, while persisting over multiple days of withdrawal. Using a novel, data-driven severity index, we find that network activity scales with individual behavioral severity without simply reflecting ongoing somatic behaviors or general aversion, suggesting that EN-Withdrawal underlies a withdrawal-induced internal state. Strikingly, network activity predicts the escalation of fentanyl self-administration on a mouse-by-mouse basis in experienced, but not drug-naive, animals. These findings reveal a neurophysiological substrate of the negative reinforcement cycle of addiction that shapes individual vulnerability.

Learning interpretable low-dimensional representations of whole-brain neuronal dynamics remains a major computational challenge in systems neuroscience. We present a wiring-agnostic deep-learning framework that couples a convolutional encoder with a temporal transformer to learn compact representations directly from volumetric calcium imaging of the entire Drosophila melanogaster brain. Trained to classify 16 experimental conditions that factorially combine metabolic state (fed, starved), sensory modality (olfaction, gustation, or combined), and stimulus valence (appetitive, aversive, or conflicting), the model organizes pan-neuronal whole-brain population activity into geometrically distinct, condition-specific clusters. Analysis of the models latent space reveals that state, modality, and valence are encoded along three near-orthogonal axes: a separable structure that emerges from the classification objective without explicit disentanglement constraints. Spatial attribution and regional importance analyses link modality decoding to distinct anatomical circuits, whereas metabolic state and valence related information show weaker regional specificity and broader distribution across the brain. Our approach does not require anatomical annotation, neuronal identification, or connectivity information, and thus provides a scalable foundation for comparative whole-brain imaging and representation learning of brain wide dynamics.

Sleep is thought to stabilize newly formed memories, yet the neural reorganization through which sleep converts learning-induced plasticity burden into stable memory remains unclear. Current state-specific oscillatory markers provide limited insight into how learning reshapes population dynamics across sleep-wake states. Using spectral parameterization of high-density EEG, we show that declarative learning redistributes frontocentral waking aperiodic regimes toward flatter slopes relative to a non-learning control. These deviations are renormalized during subsequent NREM sleep toward steeper slopes with accompanying oscillatory power shifts. Spatial deviations in waking slopes reveal a region-specific coupling with NREM, dissociable from canonical oscillatory signatures. A latent neural-memory mode showed that the wake-sleep aperiodic contrast best predicted overnight accuracy changes, whereas local oscillations and aperiodic shifts defined the spatial pattern of neural variation supporting memory stabilization. Together, these findings identify sleep-dependent recalibration of learning-perturbed population dynamics as a systems-level mechanism linking homeostatic plasticity to memory consolidation.

Experience unfolds as a stream shaped by hidden causes that change over time. Adaptive behavior requires inferring the underlying states and adjusting when they change. Yet, how neural circuits discover and track latent states remains unclear. Here we introduce NeuraGEM, a neural architecture that combines fast transient activity with slow synaptic plasticity to implement an online analogue of Expectation-Maximization. By separating timescales, NeuraGEM clusters sequential experiences, detects context changes, and stabilizes task-specific computations. The model generalizes beyond conventional recurrent networks and reproduces key features of human contextual learning, including curriculum-dependent effects. It also gives rise to population dynamics resembling those observed in brain circuits, including line-attractor structure and transient error responses at change points. Together, these findings provide a mechanistic account of how neural circuits organize experience into latent states that support rapid inference and adaptive behavior.

Neuronal ensembles--groups of neurons that exhibit coordinated activity during behavior--are a fundamental feature of cortical computation. Dendritic branches amplify clustered synaptic inputs through local nonlinearities, suggesting that presynaptic groups might organize their connections in specific spatial patterns to engage these mechanisms. Whether the same axon groups form synaptic clusters with consistent spatial arrangements across different target neurons remains unknown, but nanoscale connectomes would resolve such anatomical motifs if they exist. We analyzed millions of synaptic connections in a connectome of mouse visual cortex and found over 700,000 axon groups that repeatedly cluster their synapses onto dendritic branches of multiple pyramidal cells, with over 500,000 maintaining consistent distal-to-proximal arrangements. These repeated patterns occur far more frequently than expected from spatial proximity or layer-based connectivity rules. Axon groups preferentially target specific dendritic branches and position their synapses in stereotyped spatial configurations across multiple postsynaptic partners, revealing that functional ensembles leave characteristic anatomical signatures in cortical microarchitecture.

Among individuals with equivalent Alzheimers pathology, cognitive outcomes can diverge by decades, a phenomenon termed cognitive reserve that remains descriptive after thirty years of research. We propose that the [~]109-to-10 bits/s gap between sensory input and behavioral output functions as error-correcting redundancy in the sense of Shannons channel coding theorem. Progressive neuronal loss maps to symbol erasure in a redundant code, and the critical damage fraction at which cognition fails is dc = 1 - k/n, where k {approx} 10 bits/s is the behavioral channel requirement and n is the effective number of coding units. We evaluate this threshold across three channel models (binary erasure, Gaussian, and Erd[o]s-Renyi percolation) and show that all produce a sharp phase transition from reliable to unreliable decoding. The framework makes four testable predictions: (i) dc scales with the measurable redundancy ratio{rho} = n/k, which accounts for clinical heterogeneity; (ii) information-theoretic redundancy from resting-state fMRI should predict time-to-conversion beyond structural atrophy; (iii) the decline trajectory near dc is sharp, consistent with the "cognitive cliff"; and (iv) motor circuits, operating at higher bandwidth, have lower reserve than cognitive circuits. Significance StatementCognitive reserve (why some brains resist dementia pathology better than others) has been described for thirty years but never given a quantitative, information-theoretic foundation. We propose that the roughly hundred-million-fold gap between sensory input ([~]109 bits/s) and behavioral output ([~]10 bits/s) functions as error-correcting redundancy in the Shannon coding-theoretic sense. This yields a closed-form critical damage threshold, dc = 1 - k/n, below which cognitive function is preserved and above which it collapses; this is consistent with the clinically observed plateau-then-cliff pattern of dementia. The framework unifies cognitive reserve with channel coding theory, accounts for individual heterogeneity in disease onset, and generates falsifiable predictions that link information-theoretic redundancy measures to time-to-clinical-conversion.

Stroke is one of the leading causes of global disability, yet the principles governing how focal brain injury disrupts large-scale neural connectivity over time remain poorly understood. Here, we leverage a longitudinal multimodal dataset to track the evolution of individual-specific connectivity patterns, or brain fingerprints, over the first year after stroke. Despite a persistent shift from healthy architecture, we demonstrate that each patients unique functional connectome fingerprint is remarkably resilient and stabilizes within three weeks. This early global stabilization masks a protracted system-specific reorganization of brain circuits, which is characterized by an initial increase in connectivity within sensory and attention systems, followed by a decline across higher-level association networks. A joint structure-function embedding further shows that recovery involves a gradual shift toward the normative healthy range, driven primarily by functional reconfiguration atop a stable structural lesion. Crucially, a multivariate prediction model reveals that early functional signatures selectively forecast long-term impairment in language, executive function, and attention. Together, our results define the post-stroke brain as a shifting but constrained dynamical system, identifying early-stabilized brain patterns as biomarkers for individual recovery profiles and targets for personalized neurorehabilitation.

AO_SCPLOWBSTRACTC_SCPLOWThe volume of scientific manuscripts is rising faster than the available pool of expert reviewers, and AI tools are emerging as a possible response, ranging from frontier large language models applied directly to peer review to purpose-built multi-agent systems. Scalable, standardized benchmarks are needed to regularly evaluate how these tools compare to one another and to human reviewers. We present ReviewBench, an open-source, venue-agnostic framework that compares human and AI reviews across structure, alignment with a papers major claims, impact, and critique category. We apply ReviewBench to 145,021 review comments from human reviewers, frontier large language models (GPT-5.2, and Gemini 3 Pro), and Reviewer3.com (R3), a multi-agent peer review system. The dataset spans papers in computer science (ICLR 2025, n = 1,000), social science (Nature Human Behaviour, n = 142), and life science (eLife, n = 1,000). Across disciplines, AI reviews are more structured and engage more directly with a papers major claims, with R3 more often surfacing consequential comments, defined as comments capable of undermining those claims. When restricting to critical comments, however, human reviewers rank first on consequential rate on more individual papers than any AI source, despite a lower average. We identify a bimodal reviewer distribution with peaks near 0% and 100%, indicating that many reviewers outperform AI on this metric, but a substantial fraction of reviewers near 0% brings the average down. Critique typing demonstrates systematic differences, where humans emphasize contribution and clarity, while AI emphasizes validity, sufficiency, and transparency. Together, these findings argue against framing AI as a replacement for human review and instead support a complementary model in which AI scales technical verification of major claims while human judgment remains essential for evaluating contribution and shaping editorial decisions.

Psychedelics produce long-lasting effects, but their circuit mechanisms remain unclear. Here we show that, in rats, a single dose of lysergic acid diethylamide (LSD) persistently reduces pain affect. This effect is recapitulated by local administration in the anterior cingulate cortex (ACC), but not primary somatosensory cortex. Neuropixels recordings reveal that LSD suppresses stimulus-evoked nociceptive responses in the ACC, reducing the encoding of aversive value. Despite increasing intrinsic excitability ex vivo, LSD reduces the maximum stimulus-evoked firing of ACC neurons in vivo, indicating a dissociation between excitability and sensory encoding. Together, these findings show that psychedelics disrupt the cortical transformation of nociceptive input into aversive representations.

The human pons relays information between the brain and the body. It is affected by pathological processes, including diffuse midline gliomas (DMGs) and multiple sclerosis (MS) which predominantly arise in childhood and middle age, respectively. Although multiple studies address these disease states, a comprehensive resource for normal pons development is lacking. Here we present the first installment of PEDIA-BRAIN, an encyclopedia of gene expression and chromatin accessibility from 140,771 human pons nuclei spanning the first trimester to early adulthood, as a resource for the scientific community. Exploration of the encyclopedia identified two trajectories to mature oligodendrocytes and developmental restriction of genes for neuron to oligodendrocyte progenitor cell synapses. To illustrate the utility of the resource, we compared single cell transcriptomes from DMG and MS tissues to the encyclopedia and identified perturbation of oligodendrocyte subtypes in both diseases. Data may be accessed at https://pediabrain.nchgenomics.org.